Why my results have time delay when I use LSTM?












3












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I am trying to fit and test LSTM on a numeric series(like stock prices). But it seems that I always get a lag in predicted graph(Blue) with respect to real graph(red). Does anyone know why this happened? (I searched and realized this is a problem for others too).



enter image description here



I use Keras.sequential.LSTM library.






lstm difference







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  • 1




    $begingroup$
    It would be easier to understand your problem and help you if you can provide the code too!
    $endgroup$
    – naive
    yesterday
















3












$begingroup$


I am trying to fit and test LSTM on a numeric series(like stock prices). But it seems that I always get a lag in predicted graph(Blue) with respect to real graph(red). Does anyone know why this happened? (I searched and realized this is a problem for others too).



enter image description here



I use Keras.sequential.LSTM library.






lstm difference







share|improve this question











$endgroup$








  • 1




    $begingroup$
    It would be easier to understand your problem and help you if you can provide the code too!
    $endgroup$
    – naive
    yesterday














3












3








3





$begingroup$


I am trying to fit and test LSTM on a numeric series(like stock prices). But it seems that I always get a lag in predicted graph(Blue) with respect to real graph(red). Does anyone know why this happened? (I searched and realized this is a problem for others too).



enter image description here



I use Keras.sequential.LSTM library.






lstm difference







share|improve this question











$endgroup$




I am trying to fit and test LSTM on a numeric series(like stock prices). But it seems that I always get a lag in predicted graph(Blue) with respect to real graph(red). Does anyone know why this happened? (I searched and realized this is a problem for others too).



enter image description here



I use Keras.sequential.LSTM library.






lstm difference




lstm difference






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited yesterday









naive

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asked yesterday









user145959user145959

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485








  • 1




    $begingroup$
    It would be easier to understand your problem and help you if you can provide the code too!
    $endgroup$
    – naive
    yesterday














  • 1




    $begingroup$
    It would be easier to understand your problem and help you if you can provide the code too!
    $endgroup$
    – naive
    yesterday








1




1




$begingroup$
It would be easier to understand your problem and help you if you can provide the code too!
$endgroup$
– naive
yesterday




$begingroup$
It would be easier to understand your problem and help you if you can provide the code too!
$endgroup$
– naive
yesterday










1 Answer
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$begingroup$

I've had similar results when working with time series data. My conclusion was that the model does not learn any real pattern, except that the next value is pretty close to the previous one (or a smoothing of previous ones) + some noise.



This can be easily verified, if you feed your model's predictions recursively into the model to further predict the future (multi-step ahead forecasting based on the model's predictions). In the above mentioned case, your predictions will explode exponentially pretty quickly.






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    $begingroup$

    I've had similar results when working with time series data. My conclusion was that the model does not learn any real pattern, except that the next value is pretty close to the previous one (or a smoothing of previous ones) + some noise.



    This can be easily verified, if you feed your model's predictions recursively into the model to further predict the future (multi-step ahead forecasting based on the model's predictions). In the above mentioned case, your predictions will explode exponentially pretty quickly.






    share|improve this answer









    $endgroup$


















      1












      $begingroup$

      I've had similar results when working with time series data. My conclusion was that the model does not learn any real pattern, except that the next value is pretty close to the previous one (or a smoothing of previous ones) + some noise.



      This can be easily verified, if you feed your model's predictions recursively into the model to further predict the future (multi-step ahead forecasting based on the model's predictions). In the above mentioned case, your predictions will explode exponentially pretty quickly.






      share|improve this answer









      $endgroup$
















        1












        1








        1





        $begingroup$

        I've had similar results when working with time series data. My conclusion was that the model does not learn any real pattern, except that the next value is pretty close to the previous one (or a smoothing of previous ones) + some noise.



        This can be easily verified, if you feed your model's predictions recursively into the model to further predict the future (multi-step ahead forecasting based on the model's predictions). In the above mentioned case, your predictions will explode exponentially pretty quickly.






        share|improve this answer









        $endgroup$



        I've had similar results when working with time series data. My conclusion was that the model does not learn any real pattern, except that the next value is pretty close to the previous one (or a smoothing of previous ones) + some noise.



        This can be easily verified, if you feed your model's predictions recursively into the model to further predict the future (multi-step ahead forecasting based on the model's predictions). In the above mentioned case, your predictions will explode exponentially pretty quickly.







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered yesterday









        Andrei PoehlmannAndrei Poehlmann

        1011




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